Discipline: Ecology Environmental and Earth Sciences
Dipatrimarki I. Farkas - North Carolina Central University
Particulate matter (PM), especially those smaller than 2.5 micrometers, strongly contribute to respiratory problems in humans and reduced visibility via haze. A major problem with monitoring particulate matter in the atmosphere is that the instruments used to measure particle count or mass can only do so in their immediate vicinity due to their limited range. The purpose of this particular project is to examine the correlation between the PM2.5 data from the monitoring stations and NASA’s Aerosol Optical Depth (AOD) data from satellites in order to test if AOD data could be used to estimate PM2.5 data of the regions that do not have PM monitoring stations nearby. The process mainly consists of retrieving the AOD data from NASA web site and PM2.5 data from the Air Now website and then combining both data into a single spreadsheet for each individual month in the years 2011, 2012, and 2013 in NC. The composite spreadsheets were then scoured for days that have both AOD and PM data and these days are transposed onto a new sheet. The data was then be graphed and AOD and PM2.5 correlation was examined for each PM2.5 station in NC. Three different resolutions of AOD images were used to see which yielded the better correlation to PM 2.5. It was found that the AOD data did significantly correlate with PM2.5 data at most of the PM stations and that there was also no significant AOD-PM2.5 correlation at a few stations. The AOD-PM 2.5 correlation varied from station-to-station and across the different resolutions. The monitoring of fine particulate matter could help manage both point and non-point sources of air pollution. This could help prevent conditions that are hazardous to human health or reduce visibility.
Funder Acknowledgement(s): NSF HBCU-UP Program at NCCU
Faculty Advisor: Zhiming Yang, firstname.lastname@example.org
Role: My responsibilities include: downloading all of the necessary AOD and PM 2.5 data from each source, using GIS to filter out unnecessary images and to extract the RASTER values, compiling both the AOD and PM 2.5 data into spreadsheets, and using the spreadsheet to calculate the correlation coefficients for the various time intervals.